Pages that link to "Item:Q2135831"
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The following pages link to Adaptive deep density approximation for Fokker-Planck equations (Q2135831):
Displaying 16 items.
- Self-adaptive physics-informed neural networks (Q2112437) (← links)
- A deep domain decomposition method based on Fourier features (Q2112697) (← links)
- An efficient data-driven solver for Fokker-Planck equations: algorithm and analysis (Q2129665) (← links)
- Adaptive density tracking by quadrature for stochastic differential equations (Q2152700) (← links)
- On computing the hyperparameter of extreme learning machines: algorithm and application to computational PDEs, and comparison with classical and high-order finite elements (Q2671403) (← links)
- DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations (Q2681099) (← links)
- VAE-KRnet and Its Applications to Variational Bayes (Q5077693) (← links)
- Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow (Q5106295) (← links)
- Adaptive deep density approximation for fractional Fokker-Planck equations (Q6087826) (← links)
- Numerical computation of partial differential equations by hidden-layer concatenated extreme learning machine (Q6159015) (← links)
- AONN: An Adjoint-Oriented Neural Network Method for All-At-Once Solutions of Parametric Optimal Control Problems (Q6194971) (← links)
- Nonstandard finite difference schemes for linear and non-linear Fokker-Planck equations (Q6551319) (← links)
- Solving the non-local Fokker-Planck equations by deep learning (Q6551384) (← links)
- Deep adaptive sampling for surrogate modeling without labeled data (Q6639518) (← links)
- Navigating PINNs via maximum residual-based continuous distribution (Q6669778) (← links)
- Adaptive deep density approximation for stochastic dynamical systems (Q6671865) (← links)
